Algorithms for clustering data
Algorithms for clustering data
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Applied multivariate techniques
Applied multivariate techniques
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Interactive exploration of very large relational datasets through 3D dynamic projections
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Geometric methods and applications: for computer science and engineering
Geometric methods and applications: for computer science and engineering
Visual exploration of large data sets
Communications of the ACM
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing Data
Machine Learning
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Cluster validity methods: part I
ACM SIGMOD Record
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Non-Linear Dimensionality Reduction
Advances in Neural Information Processing Systems 5, [NIPS Conference]
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An Empirical Study on the Visual Cluster Validation Method with Fastmap
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
Inventing discovery tools: combining information visualization with data mining
Information Visualization
Validating and Refining Clusters via Visual Rendering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Cluster rendering of skewed datasets via visualization
Proceedings of the 2003 ACM symposium on Applied computing
Hypothesis oriented cluster analysis in data mining by visualization
Proceedings of the working conference on Advanced visual interfaces
iVIBRATE: Interactive visualization-based framework for clustering large datasets
ACM Transactions on Information Systems (TOIS)
Efficiently clustering transactional data with weighted coverage density
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
A dimensionality reduction algorithm and its application for interactive visualization
Journal of Visual Languages and Computing
Determining the best K for clustering transactional datasets: A coverage density-based approach
Data & Knowledge Engineering
Visual Verification of Hypotheses
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
“Best K”: critical clustering structures in categorical datasets
Knowledge and Information Systems
HE-Tree: a framework for detecting changes in clustering structure for categorical data streams
The VLDB Journal — The International Journal on Very Large Data Bases
A Visual Method for High-Dimensional Data Cluster Exploration
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Improved Visual Clustering through Unsupervised Dimensionality Reduction
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
SCALE: a scalable framework for efficiently clustering transactional data
Data Mining and Knowledge Discovery
Enhanced visual separation of clusters by M-mapping to facilitate cluster analysis
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
CloudVista: visual cluster exploration for extreme scale data in the cloud
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
iDVS: an interactive multi-document visual summarization system
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
DClusterE: A Framework for Evaluating and Understanding Document Clustering Using Visualization
ACM Transactions on Intelligent Systems and Technology (TIST)
HOV3: an approach to visual cluster analysis
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
CloudVista: interactive and economical visual cluster analysis for big data in the cloud
Proceedings of the VLDB Endowment
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Clustering is an important technique for understanding of large multi-dimensional datasets. Most of clustering research to date has been focused on developing automatic clustering algorithms and cluster validation methods. The automatic algorithms are known to work well in dealing with clusters of regular shapes, for example, compact spherical shapes, but may incur higher error rates when dealing with arbitrarily shaped clusters. Although some efforts have been devoted to addressing the problem of skewed datasets, the problem of handling clusters with irregular shapes is still in its infancy, especially in terms of dimensionality of the datasets and the precision of the clustering results considered. Not surprisingly, the statistical indices works ineffective in validating clusters of irregular shapes, too. In this paper, we address the problem of clustering and validating arbitrarily shaped clusters with a visual framework (VISTA). The main idea of the VISTA approach is to capitalize on the power of visualization and interactive feedbacks to encourage domain experts to participate in the clustering revision and clustering validation process. The VISTA system has two unique features. First, it implements a linear and reliable visualization model to interactively visualize multi-dimensional datasets in a 2D star-coordinate space. Second, it provides a rich set of user-friendly interactive rendering operations, allowing users to validate and refine the cluster structure based on their visual experience as well as their domain knowledge.